| Literature DB >> 29944078 |
Garry Choy1, Omid Khalilzadeh1, Mark Michalski1, Synho Do1, Anthony E Samir1, Oleg S Pianykh1, J Raymond Geis1, Pari V Pandharipande1, James A Brink1, Keith J Dreyer1.
Abstract
Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed. © RSNA, 2018.Mesh:
Year: 2018 PMID: 29944078 PMCID: PMC6542626 DOI: 10.1148/radiol.2018171820
Source DB: PubMed Journal: Radiology ISSN: 0033-8419 Impact factor: 11.105